Gender Pay Gap in the EU
Raquel Baltazar
Wed 25 Nov 2020 (15:53:53)
Loading Libraries
Importing the Data
query <- search_eurostat(pattern = "Gender pay gap in unadjusted form",
type = "table", fixed = FALSE)
query[, 1:2]## # A tibble: 4 x 2
## title code
## <chr> <chr>
## 1 Gender pay gap in unadjusted form sdg_05_20
## 2 Gender pay gap in unadjusted form sdg_05_20
## 3 Gender pay gap in unadjusted form tesem180
## 4 Gender pay gap in unadjusted form sdg_05_20Variables dat and dat1
dat = Every country in the EU separately
dat1 = Portugal vs EU
## # A tibble: 2 x 5
## unit nace_r2 geo time values
## <chr> <chr> <chr> <dbl> <dbl>
## 1 PC B-S_X_O AT 2002 NA
## 2 PC B-S_X_O AT 2006 25.5dat1<- get_eurostat(id="sdg_05_20", time_format = "num",
filters = list (geo = PTandEU))
dat1 [1:2,]## # A tibble: 2 x 5
## unit nace_r2 geo time values
## <chr> <chr> <chr> <dbl> <dbl>
## 1 PC B-S_X_O EU27_2020 2002 NA
## 2 PC B-S_X_O EU27_2020 2006 NA## # A tibble: 3 x 5
## unit nace_r2 geo time values
## <chr> <chr> <chr> <dbl> <dbl>
## 1 Percent… Industry, construction and services (except publ… Austr… 2002 NA
## 2 Percent… Industry, construction and services (except publ… Austr… 2006 25.5
## 3 Percent… Industry, construction and services (except publ… Austr… 2007 25.5## # A tibble: 3 x 5
## unit nace_r2 geo time values
## <chr> <chr> <chr> <dbl> <dbl>
## 1 Percent… Industry, construction and services… European Union - 2… 2002 NA
## 2 Percent… Industry, construction and services… European Union - 2… 2006 NA
## 3 Percent… Industry, construction and services… European Union - 2… 2007 NAClean the data (remove columns)
Evolution of Gender Pay Gap in EU, 2002-2018
(there is no available data for 2019)
Line graph
dat <- get_eurostat(id="sdg_05_20", filters = list(geo = ct))
library(ggplot2)
library(dplyr)
ggplot(dat,
aes(x = time, y= values, color = geo, label = geo)) +
geom_line (alpha = .5) +
geom_text (data = dat %>% group_by(geo) %>%
filter(time == max(time)),
size =2.6) +
theme(legend.position = "none") +
labs(title = "Gender Pay Gap, 2003-2018",
x= "Year", y= "%")
labs(title = 'Year: {frame_time}', x = 'Time', y = 'Percentage') +
transition_time(time) +
ease_aes('linear')## NULLThe same line graph, but animated
ggplot(dat, aes(time, values, color = geo, label = geo)) +
geom_line(alpha = .5) +
geom_text (data = dat %>% group_by(geo) %>%
filter(time == max(time)),
size =2.6) +
theme(legend.position = "none") +
scale_color_viridis_d() +
labs(x = "Year", y = "Gender Pay Gap") +
theme(legend.position = "top") +
labs(title = "Gender Pay Gap, 2002-2018",
x= "Year", y= "%") +
transition_reveal(time) +
geom_point() +
transition_reveal(time)
Portugal vs. European Union, 2006-2018
Portugal has no available data until 2006 and the EU only has available data from 2010 on (again, there is no available data for 2019)
line graph
pteu <- c("European Union - 27 countries (from 2020)","Portugal")
library(ggplot2)
library(dplyr)
ggplot(dat1,
aes(x = time, y= values, color = geo, label = geo)) +
geom_line (alpha = .5) +
geom_text (data = dat1 %>% group_by(geo) %>%
filter(time == max(time)),
size =2.6) +
theme(legend.position = "none") +
labs(title = "Gender Pay Gap, 2006-2019",
x= "Year", y= "%")
Then vs Now
Now we compare the first and the latest years [2002 vs. 2019] provided in the dataset individually.
Gender Pay Gap in 2002
Bar Chart
dat_2002 <- dat %>%
filter(time == "2002-01-01")
ggplot (dat_2002, aes(x= reorder(geo, values), y = values)) +
geom_col (color = "white", fill = "grey80") +
theme ( axis.text.x = element_text (size = 6)) +
labs (title = "Gender Pay Gap in 2002",
y = "%", x = NULL)
Map
mapdata <- get_eurostat_geospatial(nuts_level = 0) %>%
right_join (dat_2002) %>%
mutate (cat = cut_to_classes (values, n = 4, decimals = 1))
head(select(mapdata,geo,values,cat), 3)## Simple feature collection with 3 features and 3 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 5.95607 ymin: 34.56908 xmax: 34.56859 ymax: 47.80401
## geographic CRS: WGS 84
## geo values cat geometry
## 1 BG 18.9 16.9 ~< 22.3 MULTIPOLYGON (((22.99717 43...
## 2 CH NA No data MULTIPOLYGON (((8.61383 47....
## 3 CY 22.5 22.3 ~< 27.7 MULTIPOLYGON (((33.75237 34...ggplot(mapdata, aes(fill = cat)) +
scale_fill_brewer(palette = "RdYlBu") +
geom_sf (color = alpha("white", 1/3), alpha = .6) +
xlim (c(-12,44)) + ylim(c(35, 70)) +
labs( title = "Gender Pay Gap in 2002",
subtitle = "% of average gross hourly earnings of men",
fill = "%")
only a few countries have available data for 2002 Portugal only has available data from 2006 on
Let’s try using 2006 as a starting point
Bar chart
library(gghighlight)
dat_2006 <- dat %>%
filter(time == "2006-01-01")
ggplot (dat_2006, aes(x= reorder(geo, values), y = values)) +
geom_col (color = "white", fill = "tomato") +
gghighlight(geo == "PT") +
theme ( axis.text.x = element_text (size = 6)) +
labs (title = "Gender Pay Gap in 2006",
y = "%", x = NULL)
Portugal is highlighted for analysis and comparison
Map
mapdata <- get_eurostat_geospatial(nuts_level = 0) %>%
right_join (dat_2006) %>%
mutate (cat = cut_to_classes (values, n = 4, decimals = 1))
head(select(mapdata,geo,values,cat), 3)## Simple feature collection with 3 features and 3 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 5.95607 ymin: 34.56908 xmax: 34.56859 ymax: 47.80401
## geographic CRS: WGS 84
## geo values cat geometry
## 1 BG 12.4 10.8 ~< 17.1 MULTIPOLYGON (((22.99717 43...
## 2 CH 18.6 17.1 ~< 23.4 MULTIPOLYGON (((8.61383 47....
## 3 CY 21.8 17.1 ~< 23.4 MULTIPOLYGON (((33.75237 34...ggplot(mapdata, aes(fill = cat)) +
scale_fill_brewer(palette = "RdYlBu") +
geom_sf (color = alpha("white", 1/3), alpha = .6) +
xlim (c(-12,44)) + ylim(c(35, 70)) +
labs( title = "Gender Pay Gap in 2006",
subtitle = "% of average gross hourly earnings of men",
fill = "%")
## Gender Pay Gap in 2018
(again, there is no available data for 2019)
Bar Chart
dat_2018 <- dat %>%
filter(time == "2018-01-01")
ggplot (dat_2018, aes(x= reorder(geo, values), y = values)) +
geom_col (color = "white", fill = "tomato") +
gghighlight(geo == "PT") +
theme ( axis.text.x = element_text (size = 6)) +
labs (title = "Gender Pay Gap in 2018",
y = "%", x = NULL)
Map
mapdata <- get_eurostat_geospatial(nuts_level = 0) %>%
right_join (dat_2018) %>%
mutate (cat = cut_to_classes (values, n = 4, decimals = 1))
head(select(mapdata,geo,values,cat), 3)## Simple feature collection with 3 features and 3 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 5.95607 ymin: 34.56908 xmax: 34.56859 ymax: 47.80401
## geographic CRS: WGS 84
## geo values cat geometry
## 1 BG 13.9 11.6 ~< 16.7 MULTIPOLYGON (((22.99717 43...
## 2 CH NA No data MULTIPOLYGON (((8.61383 47....
## 3 CY 10.4 6.5 ~< 11.6 MULTIPOLYGON (((33.75237 34...ggplot(mapdata, aes(fill = cat)) +
scale_fill_brewer(palette = "RdYlBu") +
geom_sf (color = alpha("white", 1/3), alpha = .6) +
xlim (c(-12,44)) + ylim(c(35, 70)) +
labs( title = "Gender Pay Gap in 2018",
subtitle = "% of average gross hourly earnings of men",
fill = "%")
ANIMATIONS FROM 2002 TO 2019
Bar chart
ggplot (dat, aes(x= reorder(geo, values), y = values)) +
geom_col (color = "white", fill = "grey80") +
theme ( axis.text.x = element_text (size = 6)) +
labs( title = "Gender Pay Gap in 2019",
subtitle = "% of average gross hourly earnings of men",
fill = "%") +
labs(title = 'Year: {frame_time}') +
transition_time(time) +
ease_aes()